Adapters, a plug-in neural network module with some tunable parameters, have emerged as a parameter-efficient transfer learning technique for adapting pre-trained models to downstream tasks, especially for natural language processing (NLP) and computer vision (CV) fields. Meanwhile, learning recommendation models directly from raw item modality features -- e.g., texts of NLP and images of CV -- can enable effective and transferable recommender systems (called TransRec). In view of this, a natural question arises: can adapter-based learning techniques achieve parameter-efficient TransRec with good performance? To this end, we perform empirical studies to address several key sub-questions. First, we ask whether the adapter-based TransRec performs comparably to TransRec based on standard full-parameter fine-tuning? does it hold for recommendation with different item modalities, e.g., textual RS and visual RS. If yes, we benchmark these existing adapters, which have been shown to be effective in NLP and CV tasks, in item recommendation tasks. Third, we carefully study several key factors for the adapter-based TransRec in terms of where and how to insert these adapters? Finally, we look at the effects of adapter-based TransRec by either scaling up its source training data or scaling down its target training data. Our paper provides key insights and practical guidance on unified & transferable recommendation -- a less studied recommendation scenario. We release our codes and other materials at: https://github.com/westlake-repl/Adapter4Rec/.

翻译：适配器作为一种带有可调参数的插件式神经网络模块，已成为将预训练模型迁移至下游任务（尤其在自然语言处理与计算机视觉领域）的一种参数高效迁移学习技术。与此同时，直接利用原始物品模态特征（如自然语言处理中的文本与计算机视觉中的图像）学习推荐模型，可构建高效且可迁移的推荐系统（称为TransRec）。基于此，一个自然的问题浮现：基于适配器的学习技术能否在实现参数高效的TransRec同时保持良好的性能？为此，我们通过实证研究解决几个关键子问题。首先，我们探究基于适配器的TransRec是否与基于标准全参数微调的TransRec表现相当？这在不同物品模态的推荐（如文本推荐系统与视觉推荐系统）中是否同样成立？若成立，我们将在物品推荐任务中对比这些在自然语言处理与计算机视觉任务中已被证明有效的现有适配器。第三，我们细致研究基于适配器的TransRec在插入位置与方式上的若干关键因素。最后，我们通过扩大源训练数据或缩小目标训练数据，分析基于适配器的TransRec的影响效果。本文为统一化与可迁移推荐这一研究较少的场景提供了关键见解与实用指导。相关代码与材料已发布至：https://github.com/westlake-repl/Adapter4Rec/。